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Found 7 papers, 2 papers with code
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory.
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
We evaluate these models on spring, pendulum, gravitational, and 3D deformable solid systems to compare the performance in terms of rollout error, conserved quantities such as energy and momentum, and generalizability to unseen system sizes.
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively) encode strong inductive biases that allow them to outperform other models of physical systems significantly.
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
Neural networks with physics based inductive biases such as Lagrangian neural networks (LNN), and Hamiltonian neural networks (HNN) learn the dynamics of physical systems by encoding strong inductive biases.
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks
Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics.
Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias
However, these models still suffer from issues such as inability to generalize to arbitrary system sizes, poor interpretability, and most importantly, inability to learn translational and rotational symmetries, which lead to the conservation laws of linear and angular momentum, respectively.
Momentum Conserving Lagrangian Neural Networks
However, these models still suffer from issues such as inability to generalize to arbitrary system sizes, poor interpretability, and most importantly, inability to learn translational and rotational symmetries, which lead to the conservation laws of linear and angular momentum, respectively.
